Table of Contents
Using data available up to the: 2020-07-26
Note that it takes time for infection to cause symptoms, to get tested for SARS-CoV-2 infection, for a positive test to return and ultimately to enter the case data presented here. In other words, today’s case data are only informative of new infections about two weeks ago. This is reflected in the plots below, which are by date of infection.
Expected daily confirmed cases by region
Figure 1: The results of the latest reproduction number estimates (based on estimated confirmed cases with a date of infection on the 2020-07-02) in Russia, stratified by region, can be summarised by whether confirmed cases are likely increasing or decreasing. This represents the strength of the evidence that the reproduction number in each region is greater than or less than 1, respectively (see the methods for details). Regions with fewer than 40 confirmed cases reported on a single day are not included in the analysis (light grey).
National summary
Summary (estimates as of the 2020-07-02)
Table 1: Latest estimates (as of the 2020-07-02) of the number of confirmed cases by date of infection, the expected change in daily confirmed cases, the effective reproduction number, the doubling time (when negative this corresponds to the halving time), and the adjusted R-squared of the exponential fit. The mean and 90% credible interval is shown for each numeric estimate.| Estimate | |
|---|---|
| New confirmed cases by infection date | 6604 (6247 – 6936) |
| Expected change in daily cases | Likely increasing |
| Effective reproduction no. | 1 (1 – 1) |
| Doubling/halving time (days) | 130 (66 – 7500) |
| Adjusted R-squared | 0.56 (0.14 – 0.93) |
Confirmed cases, their estimated date of infection, and time-varying reproduction number estimates
Figure 2: A.) Confirmed cases by date of report (bars) and their estimated date of infection. B.) Time-varying estimate of the effective reproduction number. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Estimates from existing data are shown up to the 2020-07-02 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The vertical dashed line indicates the date of report generation.
Time-varying rate of growth and doubling time
Figure 3: A.) Time-varying estimate of the rate of growth, B.) Time-varying estimate of the doubling time in days (when negative this corresponds to the halving time), C.) The adjusted R-squared estimates indicating the goodness of fit of the exponential regression model (with values closer to 1 indicating a better fit). Estimates from existing data are shown up to the 2020-07-02. Light ribbon = 90% credible interval; dark ribbon = the 50% credible interval. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.
Regional Breakdown
Data availability
- Case counts by date, stratified by region, were downloaded from a public curated dataset (Mironov 2020, @NCoVUtils).
- Case onset dates were estimated using confirmed case counts by date of report and a distribution of reporting delays fitted to an international line-list (Xu et al., n.d.; Abbott et al. 2020).
Limitations
- Line-list data to inform an Russia specific estimate of the reporting delay was not available. This means that we could also not account for any regional differences.
- Regions are only included if at least 40 confirmed cases have been reported on a single day.
Summary of latest reproduction number and confirmed case count estimates by date of infection
Figure 4: Confirmed cases with date of infection on the 2020-07-02 and the time-varying estimate of the effective reproduction number (light bar = 90% credible interval; dark bar = the 50% credible interval.). Regions are ordered by the number of expected daily confirmed cases and shaded based on the expected change in daily confirmedcases. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control and a single case required for elimination.
Reproduction numbers over time in the six regions expected to have the most new confirmed cases
Figure 5: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates from existing data are shown up to the 2020-07-02 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control. The vertical dashed line indicates the date of report generation.
Confirmed cases and their estimated date of infection in the six regions expected to have the most new confirmed cases
Figure 6: Confirmed cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of new confirmed cases. Estimates from existing data are shown up to the 2020-07-02 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The vertical dashed line indicates the date of report generation.
Reproduction numbers over time in all regions
Figure 7: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates from existing data are shown up to the 2020-07-02 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The horizontal dotted line indicates the target value of 1 for the effective reproduction no. required for control. The vertical dashed line indicates the date of report generation.
Confirmed cases and their estimated date of infection in all regions
Figure 8: Confirmed cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates from existing data are shown up to the 2020-07-02 from when forecasts are shown. These should be considered indicative only. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The vertical dashed line indicates the date of report generation.
Latest estimates (as of the 2020-07-02)
Table 2: Latest estimates (as of the 2020-07-02) of the number of confirmed cases by date of infection, the effective reproduction number, and the doubling time (when negative this corresponds to the halving time) in each region. The mean and 90% credible interval is shown.| Region | New confirmed cases by infection date | Expected change in daily cases | Effective reproduction no. | Doubling/halving time (days) |
|---|---|---|---|---|
| Adygea Republic | 45 (32 – 58) | Unsure | 1.1 (0.9 – 1.3) | 37 (8.9 – -17) |
| Altai Krai | 200 (175 – 226) | Increasing | 1.3 (1.1 – 1.4) | 9.4 (6.4 – 17) |
| Altai Republic | 35 (23 – 46) | Unsure | 1 (0.8 – 1.2) | -230 (11 – -9.5) |
| Amur Oblast | 37 (23 – 47) | Unsure | 1.1 (0.8 – 1.3) | 32 (7.9 – -15) |
| Arkhangelsk Oblast | 117 (98 – 137) | Unsure | 1 (0.9 – 1.1) | -310 (22 – -19) |
| Astrakhan Oblast | 38 (26 – 49) | Unsure | 1 (0.8 – 1.3) | 69 (9.5 – -13) |
| Bashkortostan Republic | 49 (35 – 61) | Unsure | 1 (0.8 – 1.2) | 100 (11 – -14) |
| Belgorod Oblast | 48 (34 – 60) | Unsure | 0.9 (0.7 – 1.1) | -39 (19 – -9.5) |
| Bryansk Oblast | 44 (30 – 55) | Likely decreasing | 0.9 (0.7 – 1.1) | -28 (22 – -8.5) |
| Buryatia Republic | 42 (28 – 53) | Unsure | 1.1 (0.8 – 1.3) | 43 (8.8 – -15) |
| Chechen Republic | 14 (5 – 20) | Unsure | 1 (0.7 – 1.4) | 90 (5.9 – -6.9) |
| Chelyabinsk Oblast | 130 (108 – 149) | Unsure | 1 (0.9 – 1.1) | -150 (24 – -18) |
| Chuvashia Republic | 66 (50 – 80) | Unsure | 1 (0.9 – 1.2) | 86 (12 – -17) |
| Dagestan Republic | 51 (35 – 62) | Unsure | 0.9 (0.8 – 1.1) | -35 (20 – -9.4) |
| Ingushetia Republic | 25 (14 – 34) | Unsure | 1 (0.7 – 1.2) | -77 (10 – -7.9) |
| Irkutsk Oblast | 242 (207 – 271) | Unsure | 1 (0.9 – 1.1) | 130 (23 – -36) |
| Ivanovo Oblast | 69 (51 – 83) | Unsure | 1 (0.8 – 1.1) | -3400 (15 – -15) |
| Kabardino-Balkarian Republic | 46 (34 – 58) | Unsure | 1 (0.8 – 1.2) | -100 (13 – -11) |
| Kaliningrad Oblast | 17 (8 – 23) | Unsure | 0.9 (0.6 – 1.3) | -56 (7.9 – -6.3) |
| Kalmykia Republic | 37 (24 – 48) | Likely decreasing | 0.8 (0.6 – 1.1) | -12 (1900 – -5.7) |
| Kaluga Oblast | 50 (35 – 61) | Unsure | 1 (0.8 – 1.2) | 230 (12 – -14) |
| Kamchatka Krai | 44 (30 – 55) | Unsure | 1 (0.8 – 1.2) | -100 (14 – -11) |
| Karachay-Cherkess Republic | 36 (23 – 48) | Likely decreasing | 0.8 (0.6 – 1) | -12 (200 – -5.7) |
| Karelia Republic | 37 (25 – 48) | Unsure | 1 (0.8 – 1.2) | -2900 (10 – -10) |
| Kemerovo Oblast | 68 (51 – 82) | Likely increasing | 1.1 (0.9 – 1.3) | 29 (9.4 – -27) |
| Khabarovsk Krai | 97 (80 – 115) | Unsure | 1.1 (0.9 – 1.2) | 49 (13 – -28) |
| Khakassia Republic | 43 (30 – 55) | Unsure | 1.1 (0.9 – 1.3) | 29 (8.2 – -19) |
| Khanty-Mansi Autonomous Okrug | 287 (252 – 320) | Unsure | 1 (0.9 – 1.1) | 69 (21 – -56) |
| Kirov Oblast | 55 (41 – 70) | Likely increasing | 1.1 (0.9 – 1.3) | 18 (7.4 – -37) |
| Komi Republic | 79 (60 – 95) | Unsure | 1.1 (0.9 – 1.2) | 29 (10 – -30) |
| Kostroma Oblast | 23 (13 – 31) | Unsure | 0.9 (0.7 – 1.2) | -38 (11 – -7.1) |
| Krasnodar Krai | 65 (51 – 79) | Unsure | 1 (0.9 – 1.2) | 71 (12 – -19) |
| Krasnoyarsk Krai | 139 (116 – 161) | Unsure | 1 (0.9 – 1.1) | 110 (18 – -27) |
| Kurgan Oblast | 61 (46 – 75) | Likely increasing | 1.2 (1 – 1.5) | 12 (6.3 – 390) |
| Kursk Oblast | 46 (31 – 57) | Unsure | 0.9 (0.7 – 1.1) | -38 (17 – -9.2) |
| Leningrad Oblast | 52 (38 – 65) | Unsure | 1 (0.8 – 1.2) | -620 (13 – -13) |
| Lipetsk Oblast | 36 (24 – 48) | Unsure | 1 (0.8 – 1.2) | -480 (11 – -10) |
| Magadan Oblast | 36 (22 – 45) | Likely increasing | 1.3 (1 – 1.6) | 11 (5 – -86) |
| Mari El Republic | 31 (18 – 40) | Unsure | 1 (0.8 – 1.3) | 87 (8.5 – -11) |
| Mordovia Republic | 39 (25 – 50) | Unsure | 1 (0.8 – 1.3) | 88 (9.9 – -13) |
| Moscow | 680 (613 – 741) | Unsure | 1 (0.9 – 1.1) | 150 (34 – -62) |
| Moscow Oblast | 224 (193 – 251) | Decreasing | 0.9 (0.8 – 1) | -24 (-200 – -13) |
| Murmansk Oblast | 164 (143 – 185) | Likely increasing | 1.1 (1 – 1.2) | 30 (13 – -87) |
| Nizhny Novgorod Oblast | 232 (196 – 260) | Unsure | 1 (1 – 1.1) | 67 (19 – -47) |
| North Ossetia - Alania Republic | 24 (14 – 33) | Unsure | 1 (0.7 – 1.2) | -66 (9.9 – -7.8) |
| Novgorod Oblast | 41 (29 – 53) | Unsure | 1 (0.8 – 1.2) | -120 (13 – -10) |
| Novosibirsk Oblast | 111 (93 – 129) | Unsure | 1 (0.9 – 1.1) | 120 (17 – -23) |
| Omsk Oblast | 90 (73 – 106) | Unsure | 1 (0.9 – 1.2) | 79 (14 – -22) |
| Orel Oblast | 49 (37 – 63) | Unsure | 1.1 (0.9 – 1.3) | 26 (8.2 – -22) |
| Orenburg Oblast | 92 (73 – 108) | Likely increasing | 1.1 (0.9 – 1.2) | 37 (12 – -31) |
| Penza Oblast | 58 (42 – 72) | Likely decreasing | 0.9 (0.8 – 1.1) | -26 (32 – -9) |
| Perm Krai | 64 (50 – 81) | Unsure | 1.1 (0.9 – 1.2) | 42 (10 – -21) |
| Primorsky Krai | 59 (44 – 72) | Likely decreasing | 0.9 (0.7 – 1.1) | -29 (29 – -9.6) |
| Pskov Oblast | 33 (20 – 44) | Likely decreasing | 0.9 (0.6 – 1.1) | -16 (28 – -6.4) |
| Rostov Oblast | 99 (78 – 115) | Unsure | 1 (0.9 – 1.2) | 73 (14 – -23) |
| Ryazan Oblast | 40 (28 – 51) | Unsure | 1 (0.8 – 1.2) | -700 (11 – -11) |
| Saint Petersburg | 308 (264 – 342) | Increasing | 1.1 (1 – 1.2) | 28 (15 – 580) |
| Sakha (Yakutiya) Republic | 50 (38 – 64) | Unsure | 1 (0.8 – 1.2) | 160 (12 – -14) |
| Sakhalin Oblast | 63 (46 – 76) | Increasing | 1.3 (1 – 1.5) | 10 (5.7 – 60) |
| Samara Oblast | 88 (71 – 105) | Unsure | 1 (0.9 – 1.2) | 64 (13 – -22) |
| Saratov Oblast | 95 (75 – 111) | Unsure | 1 (0.8 – 1.1) | -91 (23 – -15) |
| Sevastopol | 5 (0 – 8) | Unsure | 1.2 (0.5 – 2) | 16 (2.7 – -4) |
| Smolensk Oblast | 43 (29 – 55) | Unsure | 1 (0.8 – 1.2) | 75 (9.8 – -13) |
| Stavropol Krai | 86 (70 – 103) | Unsure | 1 (0.9 – 1.2) | 570 (17 – -17) |
| Sverdlovsk Oblast | 286 (247 – 317) | Increasing | 1.1 (1 – 1.2) | 32 (15 – -680) |
| Tambov Oblast | 44 (31 – 56) | Unsure | 1 (0.8 – 1.2) | -2000 (12 – -12) |
| Tatarstan Republic | 38 (25 – 48) | Unsure | 1 (0.8 – 1.2) | -180 (12 – -10) |
| Tomsk Oblast | 64 (49 – 78) | Unsure | 1 (0.9 – 1.2) | 61 (11 – -18) |
| Tula Oblast | 67 (50 – 80) | Unsure | 1 (0.8 – 1.1) | -84 (18 – -12) |
| Tver Oblast | 36 (23 – 46) | Unsure | 0.9 (0.7 – 1.1) | -29 (17 – -8) |
| Tyumen Oblast | 81 (63 – 96) | Unsure | 1 (0.9 – 1.2) | 89 (14 – -19) |
| Tyva Republic | 59 (44 – 72) | Decreasing | 0.8 (0.6 – 0.9) | -10 (-32 – -6.1) |
| Udmurt Republic | 35 (24 – 47) | Likely increasing | 1.2 (1 – 1.6) | 12 (5.4 – -38) |
| Ulyanovsk Oblast | 107 (89 – 125) | Unsure | 1 (0.9 – 1.1) | 430 (18 – -20) |
| Vladimir Oblast | 48 (34 – 60) | Unsure | 1 (0.8 – 1.2) | -420 (12 – -12) |
| Volgograd Oblast | 93 (76 – 110) | Unsure | 1 (0.9 – 1.1) | 780 (17 – -18) |
| Vologda Oblast | 28 (17 – 37) | Unsure | 1.1 (0.8 – 1.4) | 20 (6.2 – -16) |
| Voronezh Oblast | 133 (109 – 151) | Likely decreasing | 0.9 (0.8 – 1) | -33 (61 – -13) |
| Yamalo-Nenets Autonomous Okrug | 200 (178 – 227) | Unsure | 1 (0.9 – 1.1) | 400 (25 – -28) |
| Yaroslavl Oblast | 42 (30 – 53) | Unsure | 1 (0.8 – 1.2) | 350 (11 – -12) |
| Zabaykalsky Krai | 43 (30 – 55) | Unsure | 0.9 (0.7 – 1.1) | -30 (19 – -8.6) |
Abbott, Sam, Joel Hellewell, James D. Munday, and Sebastian Funk. 2020. “NCoVUtils: Utility Functions for the 2019-Ncov Outbreak.” - - (-): –. https://doi.org/10.5281/zenodo.3635417.
Mironov, Sergey. 2020. “COVID-19 Data from Jhu Csse, Updated with Details on Russian Regions.” Github Repository. https://github.com/grwlf/COVID-19_plus_Russia.
Xu, Bo, Bernardo Gutierrez, Sarah Hill, Samuel Scarpino, Alyssa Loskill, Jessie Wu, Kara Sewalk, et al. n.d. “Epidemiological Data from the nCoV-2019 Outbreak: Early Descriptions from Publicly Available Data.” http://virological.org/t/epidemiological-data-from-the-ncov-2019-outbreak-early-descriptions-from-publicly-available-data/337.